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Your search keyword '"Liu, Jianbo"' showing total 30 results
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30 results on '"Liu, Jianbo"'

28. Lower Ordovician Stromatolites from the Anhui Province of South China: Construction and Geobiological Significance.

Author

Adachi, Natsuko, Ezaki, Yoichi, Liu, Jianbo, and Cao, Jun

Abstract

The Cambrian through Early Ordovician was a lengthy interval when microbialites once again dominated after their decline from a peak in the Middle Proterozoic (e.g., Riding 2006a). These microbialites declined again in the Middle–Late Ordovician, although they remained locally common (e.g., Webby 2002; Shapiro 2004; Riding 2006a). [ABSTRACT FROM AUTHOR]

Published
2011
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29. Spatio-temporal Just Noticeable Distortion Model Guided Video Watermarking.

Author

Niu, Yaqing, Liu, Jianbo, Krishnan, Sridhar, and Zhang, Qin

Abstract

Perceptual video watermarking needs to take full advantage of the results from human visual system (HVS) studies. Since motion is a specific feature of video, temporal HVS properties need to be taken into account. In this paper, we exploit a combined Spatio-Temporal Just Noticeable Distortion (JND) model which incorporates spatial CSF, temporal modulation factor, retinal velocity, luminance adaptation and contrast masking to guide watermarking for digital video. The proposed watermarking scheme, where visual models representing additional accurate perceptual visibility threshold are fully used to determine scene-driven upper bounds on watermark insertion, allows us to provide the maximum strength transparent watermark. Experimental results confirm the improved performance of our combined Spatio-Temporal JND model guided watermarking scheme. Our Spatio-Temporal JND model guided watermarking scheme which allows higher injected-watermark energy without jeopardizing picture quality performs much better on robustness than other algorithms based on the relevant existing perceptual models. [ABSTRACT FROM AUTHOR]

Published
2009
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30. Growing Structure Multiple Model System Based Anomaly Detection for Crankshaft Monitoring.

Author

Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Liu, Jianbo, Sun, Pu, Djurdjanovic, Dragan, Marko, Kenneth, and Ni, Jun

Abstract

While conventional approaches to diagnostics focus on detecting and identifying situations or behaviors which have previously been known to occur or can be anticipated, anomaly detection focuses on detecting and quantifying deviations away from learned "normal" behavior. A new anomaly detection scheme based on Growing Structure Multiple Model System(GSMMS) is utilized in this paper to detect and quantify the effects of slowly evolving anomalies on the crankshaft dynamics in a internal combustion engine. The Voronoi sets defined by the reference vectors of the growing Self-Organizing Networks(SONs), on which the GSMMS is based, naturally form a partition of the system operation space. Regionalization of system operation space using SONs makes it possible to model the system dynamics locally using simple models. In addition, the residual errors can be analyzed locally to accommodate unequally distributed residual errors in different regions. [ABSTRACT FROM AUTHOR]

Published
2006
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